اراﺋﮥ ﯾﮏ ﺳﺎﻣﺎﻧﮥ ﭘﯿﺸﻨﻬﺎدﮔﺮ ﺣﺎﻓﻈﻪﭘﺎﯾﮥ ﺗﺮﮐﯿﺒﯽ ﺑﺎ اﺳﺘﻔﺎده از ﻫﺴﺘﺎنﺷﻨﺎﺳﯽ و ﻣﺤﺘﻮا

A New WordNet Enriched Content-Collaborative Recommender System

ﺳﺎﻣﺎﻧﻪﻫﺎي ﭘﯿﺸﻨﻬﺎدﮔﺮ در زﻣﯿﻨﻪ ﺗﺠﺎرت اﻟﮑﺘﺮوﻧﯿﮏ ﺷﻨﺎﺧﺘﻪ ﺷﺪه ﻫﺴﺘﻨﺪ. از اﯾﻦﮔﻮﻧﻪ ﺳﺎﻣﺎﻧﻪﻫﺎ اﻧﺘﻈﺎر ﻣﯽرود ﮐﻪ ﮐﺎﻻﻫﺎ و اﻗﻼم ﻣﻬﻤﯽ )از ﺟﻤﻠﻪ ﻣﻮﺳﯿﻘﯽ و ﻓﯿﻠﻢ( را ﺑﻪ ﻣﺸﺘﺮﯾﺎن ﭘﯿﺸﻨﻬﺎد دﻫﻨﺪ. در ﺳﺎﻣﺎﻧﻪﻫﺎي ﭘﯿﺸﻨﻬﺎدﮔﺮ ﺳﻨﺘﯽ از ﺟﻤﻠﻪ روشﻫﺎي ﭘﺎﻻﯾﺶ ﻣﺤﺘﻮا ﭘﺎﯾﻪ و ﭘﺎﻻﯾﺶ ﻣﺸﺎرﮐﺘﯽ، ﭼﺎﻟﺶﻫﺎ و ﻣﺸﮑﻼت ﻣﻬﻤﯽ از ﺟﻤﻠﻪ ﺷﺮوع ﺳﺮد، ﻣﻘﯿﺎسﭘﺬﯾﺮي و ﭘﺮاﮐﻨﺪﮔﯽ دادهﻫﺎ وﺟﻮد دارد. اﺧﯿﺮاً ﺑﻪﮐﺎرﮔﯿﺮي روشﻫﺎي ﺗﺮﮐﯿﺒﯽ ﺗﻮاﻧﺴﺘﻪ ﺑﺎ ﺑﻬﺮهﮔﯿﺮي از ﻣﺰاﯾﺎي اﯾﻦ روشﻫﺎ ﺑﺎ ﻫﻢ، ﺑﺮﺧﯽ از اﯾﻦ ﭼﺎﻟﺶﻫﺎ را ﺗﺎ ﺣﺪ ﻗﺎﺑﻞ ﻗﺒﻮﻟﯽ ﺣﻞ ﮐﻨﻨﺪ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺳﻌﯽ ﻣﯽ ﺷﻮد روﺷﯽ ﺑﺮاي ﭘﯿﺸﻨﻬﺎد اراﺋﻪ ﺷﻮد ﮐﻪ ﺗﺮﮐﯿﺒﯽ از دو روش ﭘﺎﻻﯾﺶ ﻣﺤﺘﻮا ﭘﺎﯾﻪ و ﭘﺎﻻﯾﺶ ﻣﺸﺎرﮐﺘﯽ )ﺷﺎﻣﻞ دو روﯾﮑﺮد ﺣﺎﻓﻈﻪ ﭘﺎﯾﻪ و ﻣﺪل ﭘﺎﯾﻪ( ﺑﺎﺷﺪ. روش ﭘﺎﻻﯾﺶ ﻣﺸﺎرﮐﺘﯽ ﺣﺎﻓﻈﻪ ﭘﺎﯾﻪ، دﻗﺖ ﺑﺎﻻﯾﯽ دارد؛ اﻣﺎ ﻣﻘﯿﺎسﭘﺬﯾﺮي ﮐﻤﯽ دارد. در ﻣﻘﺎﺑﻞ، روﯾﮑﺮد ﻣﺪل ﭘﺎﯾﻪ داراي دﻗﺖ ﮐﻤﯽ در اراﺋﻪ ﭘﯿﺸﻨﻬﺎد ﺑﻪ ﮐﺎرﺑﺮان ﺑﻮده، اﻣﺎ ﻣﻘﯿﺎسﭘﺬﯾﺮي ﺑﺎﻻﯾﯽ از ﺧﻮد ﻧﺸﺎن ﻣﯽدﻫﺪ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺳﺎﻣﺎﻧﻪ ﭘﯿﺸﻨﻬﺎدﮔﺮ ﺗﺮﮐﯿﺒﯽ ﻣﺒﺘﻨﯽ ﺑﺮ ﻫﺴﺘﺎنﺷﻨﺎﺳﯽ اراﺋﻪ ﺷﺪه ﮐﻪ از ﻣﺰاﯾﺎي ﻫﺮ دو روش ﺑﻬﺮه ﺑﺮده و ﺑﺮاﺳﺎس رﺗﺒﻪﺑﻨﺪيﻫﺎي واﻗﻌﯽ، ﻣﻮرد ارزﯾﺎﺑﯽ ﻗﺮار ﻣﯽﮔﯿﺮد. ﻫﺴﺘﺎنﺷﻨﺎﺳﯽ، ﺗﻮﺻﯿﻔﯽ واﺿﺢ و رﺳﻤﯽ ﺑﺮاي ﺗﻌﺮﯾﻒ ﯾﮏ ﭘﺎﯾﮕﺎه داﻧﺶ ﺷﺎﻣﻞ ﻣﻔﺎﻫﯿﻢ )ﮐﻼسﻫﺎ( در ﺣﻮزه ﻣﻮﺿﻮﻋﯽ، ﻧﻘﺶﻫﺎ )راﺑﻂﻫﺎ( ﺑﯿﻦ ﻧﻤﻮﻧﻪﻫﺎي ﻣﻔﺎﻫﯿﻢ، ﻣﺤﺪودﯾﺖﻫﺎي ﻣﺮﺑﻮط ﺑﻪ راﺑﻄﻪﻫﺎ، ﻫﻤﺮاه ﺑﺎ ﯾﮏ ﻣﺠﻤﻮﻋﻪ از ﻋﻨﺎﺻﺮ و اﻋﻀﺎ )ﯾﺎ ﻧﻤﻮﻧﻪﻫﺎ( اﺳﺖ ﮐﻪ ﯾﮏ ﭘﺎﯾﮕﺎه داﻧﺶ را ﺗﻌﺮﯾﻒ ﻣﯽﮐﻨﺪ. ﻫﺴﺘﺎنﺷﻨﺎﺳﯽ در ﺑﺨﺶ ﭘﺎﻻﯾﺶ ﻣﺤﺘﻮا ﭘﺎﯾﻪ ﻣﻮرد اﺳﺘﻔﺎده ﻗﺮار ﻣﯽﮔﯿﺮد و ﺳﺎﺧﺘﺎر ﻫﺴﺘﺎنﺷﻨﺎﺳﯽ ﺑﻪوﺳﯿﻠﮥ روشﻫﺎي ﭘﺎﻻﯾﺶ ﻣﺸﺎرﮐﺘﯽ ﺑﻬﺒﻮد ﻣﯽﯾﺎﺑﺪ. در روش اراﺋﻪﺷﺪه در اﯾﻦ ﭘﮋوﻫﺶ، ﻋﻤﻠﮑﺮد ﺳﺎﻣﺎﻧﮥ ﭘﯿﺸﻨﻬﺎدي ﺑﻬﺘﺮ از ﻋﻤﻠﮑﺮد ﭘﺎﻻﯾﺶ ﻣﺤﺘﻮا ﭘﺎﯾﻪ و ﻣﺸﺎرﮐﺘﯽ اﺳﺖ. روش ﭘﯿﺸﻨﻬﺎدي ﺑﺎ اﺳﺘﻔﺎده از ﯾﮏ ﻣﺠﻤﻮﻋﻪداده واﻗﻌﯽ ارزﯾﺎﺑﯽ ﺷﺪه اﺳﺖ و ﻧﺘﺎﯾﺞ آزﻣﺎﯾﺶﻫﺎ ﻧﺸﺎن ﻣﯽدﻫﺪ روش ﯾﺎدﺷﺪه ﮐﺎراﯾﯽ ﺑﻬﺘﺮي دارد. ﻫﻤﭽﻨﯿﻦ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ راهﮐﺎرﻫﺎي اراﺋﻪﺷﺪه در ﻣﻘﺎﻟﻪ ﺣﺎﺿﺮ، ﻣﺸﺨﺺ ﺷﺪ، روش ﭘﯿﺸﻨﻬﺎدي دﻗﺖ و ﻣﻘﯿﺎس ﭘﺬﯾﺮي ﻣﻨﺎﺳﺒﯽ ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﻣﺎﻧﻪﻫﺎي ﭘﯿﺸﻨﻬﺎدﮔﺮي دارد ﮐﻪ ﺗﻨﻬﺎً ﺣﺎﻓﻈﻪ ﭘﺎﯾﻪ )KNN( و ﯾﺎ ﻣﺪل ﭘﺎﯾﻪ ﻫﺴﺘﻨﺪ.

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.